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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids
Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann, et al.
Computers & Chemical Engineering (2023) Vol. 171, pp. 108153-108153
Open Access | Times Cited: 46

Showing 1-25 of 46 citing articles:

Chemprop: A Machine Learning Package for Chemical Property Prediction
Esther Heid, Kevin P. Greenman, Yunsie Chung, et al.
Journal of Chemical Information and Modeling (2023) Vol. 64, Iss. 1, pp. 9-17
Open Access | Times Cited: 163
SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients
Benedikt Winter, Clemens Winter, Timm Esper, et al.
Fluid Phase Equilibria (2023) Vol. 568, pp. 113731-113731
Open Access | Times Cited: 34
Physical pooling functions in graph neural networks for molecular property prediction
Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, et al.
Computers & Chemical Engineering (2023) Vol. 172, pp. 108202-108202
Open Access | Times Cited: 33
Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications
Zhen Song, Jiahui Chen, Jie Cheng, et al.
Chemical Reviews (2023) Vol. 124, Iss. 2, pp. 248-317
Closed Access | Times Cited: 32
Understanding the language of molecules: predicting pure component parameters for the PC-SAFT equation of state from SMILES
Benedikt Winter, Philipp Rehner, Timm Esper, et al.
Digital Discovery (2025)
Open Access | Times Cited: 1
Capturing molecular interactions in graph neural networks: a case study in multi-component phase equilibrium
Shiyi Qin, Shengli Jiang, Jianping Li, et al.
Digital Discovery (2022) Vol. 2, Iss. 1, pp. 138-151
Open Access | Times Cited: 29
Gibbs–Duhem-informed neural networks for binary activity coefficient prediction
Jan G. Rittig, Kobi Felton, Alexei A. Lapkin, et al.
Digital Discovery (2023) Vol. 2, Iss. 6, pp. 1752-1767
Open Access | Times Cited: 21
Gibbs–Helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution
Edgar Iván Sánchez Medina, Steffen Linke, Martin Stoll, et al.
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 781-798
Open Access | Times Cited: 18
ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction
Kobi Felton, Lukas Raßpe-Lange, Jan G. Rittig, et al.
Chemical Engineering Journal (2024) Vol. 492, pp. 151999-151999
Open Access | Times Cited: 8
Graph neural networks for surfactant multi-property prediction
Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya, et al.
Colloids and Surfaces A Physicochemical and Engineering Aspects (2024) Vol. 694, pp. 134133-134133
Open Access | Times Cited: 7
Mapping the application research on machine learning in the field of ionic liquids: A bibliometric analysis
Ze Wei, Fei Chen, Hui Liu, et al.
Fluid Phase Equilibria (2024) Vol. 583, pp. 114117-114117
Closed Access | Times Cited: 7
Thermodynamics-consistent graph neural networks
Jan G. Rittig, Alexander Mitsos
Chemical Science (2024)
Open Access | Times Cited: 7
Predicting solvation free energies for neutral molecules in any solvent with openCOSMO-RS
Simon Müller, Thomas Nevolianis, Miquel García‐Ratés, et al.
Fluid Phase Equilibria (2024), pp. 114250-114250
Open Access | Times Cited: 6
A Deep Learning-based Framework Towards inverse Green Solvent Design for Extractive Distillation with Multi-index Constraints
Jun Zhang, Qin Wang, Mario R. Eden, et al.
Computers & Chemical Engineering (2023) Vol. 177, pp. 108335-108335
Closed Access | Times Cited: 16
Prediction and Interpretability of Melting Points of Ionic Liquids Using Graph Neural Networks
Haijun Feng, Lanlan Qin, Bingxuan Zhang, et al.
ACS Omega (2024) Vol. 9, Iss. 14, pp. 16016-16025
Open Access | Times Cited: 4
A critical methodological revisit on group-contribution based property prediction of ionic liquids with machine learning
P.-L. Cao, Jiahui Chen, Guzhong Chen, et al.
Chemical Engineering Science (2024) Vol. 298, pp. 120395-120395
Closed Access | Times Cited: 4
Applications of Predictive Modeling for Various Properties of Ionic Liquids
Shahram Lotfi, Shahin Ahmadi, Parvin Kumar, et al.
Challenges and advances in computational chemistry and physics (2025), pp. 205-229
Closed Access
Predicting the temperature-dependent CMC of surfactant mixtures with graph neural networks
Christoforos Brozos, Jan G. Rittig, Elie Akanny, et al.
Computers & Chemical Engineering (2025), pp. 109085-109085
Open Access
Recent Advances in the Modeling of Ionic Liquids Using Artificial Neural Networks
Adrian Racki, Kamil Paduszyński
Journal of Chemical Information and Modeling (2025)
Open Access
Limeade: Let integer molecular encoding aid
Shiqiang Zhang, Christian Feldmann, Frederik Sandfort, et al.
Computers & Chemical Engineering (2025), pp. 109115-109115
Closed Access
GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures
Marco Hoffmann, Hans Hasse, Fabian Jirasek
Chemical Engineering Journal Advances (2025), pp. 100750-100750
Open Access
Review on Graph Neural Networks for Process Soft Sensor Development, Fault Diagnosis, and Process Monitoring
Mingwei Jia, Yuan Yao, Yi Liu
Industrial & Engineering Chemistry Research (2025)
Closed Access
Multi-fidelity graph neural networks for predicting toluene/water partition coefficients
Thomas Nevolianis, Jan G. Rittig, Alexander Mitsos, et al.
(2024)
Open Access | Times Cited: 3
A real-time temperature field prediction method for steel rolling heating furnaces based on graph neural networks
Bo Yang, Lei Liu, Haoping Huang, et al.
International Journal of Heat and Mass Transfer (2024) Vol. 235, pp. 126220-126220
Closed Access | Times Cited: 3
Chemprop: A Machine Learning Package for Chemical Property Prediction
Esther Heid, Kevin P. Greenman, Yunsie Chung, et al.
(2023)
Open Access | Times Cited: 9
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